我想知道如何从距离矩阵中提取第一个对角线的值。
例如:
> mymatrix
[,1] [,2]
[1,] 1 2
[2,] 3 4
[3,] 6 4
[4,] 8 6
> dist(mymatrix)
1 2 3
2 2.828427
3 5.385165 3.000000
4 8.062258 5.385165 2.828427
我想在矢量中输入值2.828427, 3.000000, 2.828427
谢谢!
答案 0 :(得分:9)
一种解决方法是将<a id="div1OpenButton" class="openButton" onClick="openDiv(this)">div1</a>
<script>
function openDiv(e){
document.getElementById(e.innerHTML).style.width= '20px'
}
</script>
对象转换为dist
,然后提取行索引比列索引大1的元素:
matrix
答案 1 :(得分:0)
一种看似复杂但非常有效的解决方案,用于提取“ dist”矩阵的第d
次对角线。
subdiag <- function (dist_obj, d) {
if (!inherits(dist_obj, "dist")) stop("please provide a 'dist' object!")
n <- attr(dist_obj, "Size")
if (d < 1 || d > (n - 1)) stop(sprintf("'d' needs be between 1 and %d", n - 1L))
j_1 <- c(0, seq.int(from = n - 1, by = -1, length = n - d - 1))
subdiag_ind <- d + cumsum(j_1)
dist_obj[subdiag_ind]
}
有关“ dist”对象的打包存储的详细信息,请参见R - How to get row & column subscripts of matched elements from a distance matrix。在此函数内,j_1
是第X
列中“ (j - 1)
”的编号。 cumsum
给出主要对角线的一维索引(其值均为零)。进一步偏移d
可以得到d
次对角线的一维索引。
set.seed(0)
x <- dist(matrix(runif(10), 5))
# 1 2 3 4
#2 0.9401067
#3 0.9095143 0.1162289
#4 0.5618382 0.3884722 0.3476762
#5 0.4275871 0.6968296 0.6220650 0.3368478
subdiag(x, 1)
#[1] 0.9401067 0.1162289 0.3476762 0.3368478
lapply(1:4, subdiag, dist_obj = x)
#[[1]]
#[1] 0.9401067 0.1162289 0.3476762 0.3368478
#
#[[2]]
#[1] 0.9095143 0.3884722 0.6220650
#
#[[3]]
#[1] 0.5618382 0.6968296
#
#[[4]]
#[1] 0.4275871
对于大到大的“ dist”矩阵都具有良好的性能。
## mimic a "dist" object without actually calling function `dist`
n <- 2000
x <- structure(numeric(n * (n - 1) / 2), class = "dist", Size = n)
library(bench)
bench::mark("Psidom" = {mat = as.matrix(x); mat[row(mat) == col(mat) + 1]},
"zheyuan" = subdiag(x, 1))
## A tibble: 2 x 14
# expression min mean median max `itr/sec` mem_alloc n_gc n_itr
# <chr> <bch:tm> <bch:tm> <bch:t> <bch:tm> <dbl> <bch:byt> <dbl> <int>
#1 Psidom 553ms 553ms 553ms 552.74ms 1.81 251.8MB 5 1
#2 zheyuan 106µs 111µs 108µs 3.85ms 9045. 62.7KB 2 4519
## ... with 5 more variables: total_time <bch:tm>, result <list>, memory <list>,
## time <list>, gc <list>
subdiag
的速度提高了5120倍(553ms / 108µs),存储效率提高了4112倍(251.8MB / 62.7KB)。